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Code does not improve LLM math reasoning; structured traces do

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the impact of code on mathematical reasoning in large language models. The study found that while code improves programming abilities, it does not generally enhance mathematical reasoning and can even compete with knowledge-intensive tasks. The researchers discovered that structured reasoning traces, like math-text mixtures, are more effective for improving reasoning than executable code alone. They suggest that increasing the density of structured math-domain samples offers a targeted approach to boost mathematical reasoning without sacrificing programming performance. AI

IMPACT Clarifies which data characteristics improve LLM reasoning, suggesting more precise data-centric optimization strategies.

RANK_REASON Academic paper detailing findings on LLM training data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Code does not improve LLM math reasoning; structured traces do

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Academic paper detailing findings on LLM training data. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Enhong Chen ·

    What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code

    Code has become a standard component of modern foundation language model (LM) training, yet its role beyond programming remains unclear. We revisit the claim that code improves reasoning through controlled pretraining experiments on a 10T-token corpus with fine-grained domain sep…

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